Existing approaches to the segmentation of sensorimotor pattern sequences into causal chunks include the Modular Selection and Identification for Control (MOSAIC) model (D. M. Wolpert and M. Kawato, ‘Multiple paired forward and inverse models for motor control’, Neural Networks, 11, pp. 1317-1329, 1998) and the recurrent neural network with parametric bias (RNNPB) model (J. Tani, ‘Learning to generate articulated behavior through the bottom-up and the top-down interaction processes’, Neural Networks, 16, pp. 11-23, 2003; J. Tani, M. Ito, and Y. Sugita, ‘Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB’, Neural Networks, 17, pp. 1273-1289, 2004; U.S. Pat. No. 7,373,333; U.S. Pat. No. 7,324,980; EP1505534).
The MOSAIC model is composed of multiple modules, each of them consisting of a pair of a forward and an inverse model. Thereby, the forward models concurrently try to describe the observed patterns whereas the inverse models cooperatively contribute to the overall control of the robotic device depending on their forward models' prediction quality. If it is assumed that each forward-inverse model pair represents a schema then the differences to the present invention are as follows:                (1) Even though MOSAIC allows multiple schemata to be simultaneously active, the active schemata do not cooperatively predict future values of the state variables. Rather a concurrent prediction is applied insofar as each of the forward models predicts the state variable values of the whole system. Therefore, MOSAIC does not allow the learning or use of a combinatorial code for the description of the system dynamics, whereas the present invention does.        (2) MOSAIC proposes to use multiple forward and inverse models, more precisely, one forward-inverse model pair for each schema. In contrast, the present invention proposes to use a single processing structure (e.g. an artificial neural network) for the forward model and the inverse model, respectively. Thereby, the computational as well as the memory complexity is reduced.        (3) MOSAIC does not incorporate an explicit memory structure for the states of schemata. The present invention uses such a structure. Furthermore, the present invention uses the memorized schemata states in order to set the mode of the processing structures carrying out forward and inverse modeling. As a result the present invention allows the learning and use of a topographic organization of schemata which is not possible within the MOSAIC model.        (4) MOSAIC does not incorporate a separate processing module carrying out the recognition of schemata. Rather, MOSAIC uses the forward models insofar as the forward models' qualities in describing observed pattern sequences determines which schemata have been recognized. Therefore, the MOSAIC model does not allow a dynamic recognition of schemata insofar as whole pattern sequences have to be compared with the sequences predicted by the forward models. In contrast, the present invention allows a dynamic recognition of schemata.        (5) Lastly, MOSAIC does not incorporate the concept of a schema as a compact representation of an attractor dynamic. This means that a forward-inverse model pair of MOSAIC represents multiple dynamics; however, these dynamics do not necessarily have a common fixed point. Therefore, MOSAIC does not allow the usage of the forward-inverse model pairs for goal-directed behavior control and goal inference as the present invention does.        
The RNNPB model uses a single recurrent neural network (RNN) in which sensorimotor pattern sequences are distributely represented. It further uses parametric bias (PB) vectors as input to the RNN in order to drive the network in a certain mode. The differences between the RNNPB model and the present invention are as follows:                (1) In RNNPB a memory structure is thought to save PB vectors corresponding to certain behaviors. Upon execution of a behavior the corresponding PB vector is fed to the RNN which in turn performs the forward modeling. In contrast, the present invention proposes to use the schemata states in order to drive the network in its corresponding mode. For this reason, the present invention allows the system dynamics to be cooperatively predicted by multiple schemata.        (2) In RNNPB behaviors are recognized using the forward model. More precisely, an inverse iterative search procedure is applied in order to determine the PB vector which best describes the observed pattern sequence. In contrast, the present invention proposes to use a separate processing structure for the recognition of an attractor dynamic describing the observed behavior. As a consequence RNNPB does not allow a dynamic recognition of behavior, whereas the present invention does.        (3) RNNPB does not allow multiple behaviors to be simultaneously active since this would imply that multiple PB vectors are fed into the RNN. In contrast, the present invention allows multiple schemata to be simultaneously active. Thereby, the present invention allows the learning and use of a combinatorial schemata code which is a property RNNPB does not offer.        (4) In the RNNPB model the parameters (weight values and PB vectors) are trained in an offline fashion, whereas the present invention allows an online learning.        (5) Lastly, RNNPB does not incorporate the concept of a schema as a compact representation of an attractor dynamic.        